Abstract
Humans have always been inspired by their environment to solve their problems. When it directly imitates the behavior of living things, it is called biomimicry. Biomimicry seeks to identify winning life strategies to apply them in our world to solve challenges. It is a practice that learns from and mimics the technique used by species alive today. Fish, birds, bats, bees, fireflies, many animals, and insects provide us with a permanent demonstration of a phenomenon as simple as it is complex and will be discussed in this reading: swarms. Swarm intelligence is a subfield of computer science that draws inspiration from the behavior of swarms to solve problems. It is possible to characterize a swarm as a structured set of individuals with limited individual capacities who offer collective intelligence to solve complex problems. Swarm robotics is an application of swarm intelligence. By applying the concept to multi-robot systems, behaviors similar to those observed in the living world are reproduced and make it possible to solve problems, propose new approaches or improve existing ones. This paper reviews the swarm robotics approach from its history to its future. First, we review several Swarm Intelligence concepts to define Swarm Robotics systems, reporting their essential qualities and features and contrasting them to generic multi-robotic systems. Then, we discuss the basic idea of swarm robotics, its important features, simulators, real-life applications, and some future ideas.
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References
Beni, G., Wang, J.: Swarm Intelligence in Cellular Robotic Systems. Robots and Biological Systems: Towards New Bionics? pp. 703–712. Berlin, Springer (1993)
Cruz, D.P., Maia, R.D., De Castro, L.N.: A critical discussion into the core of swarm intelligence algorithms. Evol. Intel. 12(2), 189–200 (2019)
Schranz, M., Umlauft, M., Sende, M., Elmenreich, W.: Swarm robotic behaviors and current applications. Front. Robot. AI 7, 36 (2020)
Li, X., Clerc, M.: Swarm intelligence. In: Gendreau, M., Potvin, J. (eds.) Handbook of Metaheuristics, vol. 272, pp. 353–384. Springer, Cham (2019)
Yang, X.-S, Deb, S., Zhao, Y., Fong, S., Xingshi, He.: Swarm Intelligence: Past, Present & Future. Soft Comput. 22(2018). https://doi.org/10.1007/s00500-017-2810-5
Zoghby, N., Loscri, V., Natalizio, E., Cherfaoui, V.: Chapter 8: Robot cooperation and swarm intelligence. In: Wireless Sensor and Robot Networks: From Topology Control to Communication Aspects, pp. 163–201 (2014)
Hecker, J., Moses, M.: Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms. Swarm Intelligence (2015)
Mahendra, P., Pandey, R.: Swarm intelligence. In: International Conference on Advanced Computing. Teerthanker Mahaveer University, Moradabad (2016)
Suir, G., Rowland, M., Mayo, M.: Swarm Optimization Algorithm for Road Bypass Extrapolation (2019)
Yang, X.-S.: Particle Swarm Optimization (2021). https://doi.org/10.1016/B978-0-12-821986-7.00015-9
Oum El Fadhel Loubaba, B.: SVC device optimal location for voltage stability enhancement based on a combined particle swarm optimization-continuation power flow technique. TELKOMNIKA (Telecommun. Comput. Electron. Control) 18, 2101–2111 (2020). https://doi.org/10.12928/telkomnika.v18i4.13073
Bouhassoune, I., Chehri, A., Saadane, R., Minaoui, K.: Optimization of UHF RFID five-slotted patch tag design using PSO algorithm for biomedical sensing systems. Int. J. Environ. Res. Public Health 17(22), 8593 (2020). https://doi.org/10.3390/ijerph17228593
Tereshko, V., Loengarov, A.: Collective decision-making in honey bee foraging dynamics. Comput. Inf. Sys. J. 9(3), 1–7 (2005)
Karaboga, D.: Artificial bee colony algorithm. Scholarpedia 5(3), 6915 (2010)
Bozorg-Haddad, O., et al.: Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour. Manag. 20, 661–680 (2006)
Yang, C., Chen, J., Tu, X.: Algorithm of fast marriage in honey bees optimization and convergence analysis, pp. 1794–1799 (2007). https://doi.org/10.1109/ICAL.2007.4338865
Kn, L., et al.: Honey bees optimization algorithm for solving optimal reactive power problem. Int. J. Res. Electron. Commun. Technol. (2016)
Dorigo, M., Stützle, T.: Ant Colony Optimization: Overview and Recent Advances. Handbook of Metaheuristics, pp. 311–351. Springer, Cham (2019)
Mahapatra, G., et al.: A study of bacterial foraging optimization algorithm and its applications to solve simultaneous equations. Int. J. Comp. Appl. 72, 1–6 (2013)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)
Shen, L., Huang, X., Fan, C.: Double-group particle swarm optimization and its application in remote sensing image segmentation. Sensors 18, 1393 (2018)
Huang, X., et al.: Exploration in extreme environments with swarm robotic system. In: International Conference on Mechatronics (ICM), vol. 1, pp. 193–198 (2019)
Sahin, E.: Swarm Robotics: From Sources of Inspiration to Domains of Application, vol. 3342, pp. 10–20. Springer, Heidelberg (2004)
Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
Liekna, A., Grundspenkis, J., et al.: Towards practical application of swarm robotics: overview of swarm tasks. Eng. Rural Dev. 13, 271–277 (2014)
Chen, L., Li, Y.: Intelligent autonomous pollination for future farming—a micro air vehicle conceptual framework with artificial intelligence and human-in-the-loop. IEEE Access 1–1. https://doi.org/10.1109/ACCESS.2019.2937171
Ball, D., Ross, P., English, A., Patten, T., Upcroft, B., Fitch, R., et al.: Robotics for Sustainable Broad-Acre Agriculture, pp. 439–453. Springer (2015)
Mizokami, K.: The Pentagon’s Autonomous Swarming Drones Are the Most Unsettling Thing You’ll See Today (2017). https://www.popularmechanics.com/military/aviation/a24675/pentagon-autonomous-swarming-drones/. Accessed Januray 2022
Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., et al.: The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, pp. 59–65 (2009)
Roldán Gómez, et al.: SwarmCity Project: Can an Aerial Swarm Monitor Traffic in a Smart City? (2019). https://doi.org/10.1109/PERCOMW.2019.8730677
Zahugi, E.M.H., Shanta, M.M., Prasad, T.V.: Oil spill cleaning up using swarm of robots. In: Advances in Intelligent Systems and Computing, vol. 178. Springer (2013)
Warnat-Herresthal, S., Schultze, H., Shastry, K.L., et al.: Swarm Learning for decentralized and confidential clinical machine learning. Nature 594, 265–270 (2021)
Stirling, T., et al.: Indoor navigation with a swarm of flying robots. In: Proceedings—IEEE International Conference on Robotics and Automation, pp. 4641–4647 (2012)
Nguyen, L.A., et al.: Swarmathon: a swarm robotics experiment for future space exploration. In: International Symposium on Measurement and Control in Robotics (ISMCR) (2019)
Chehri, A., Jeon, G., Fofana, I., Imran, A., Saadane, R.: Accelerating power grid monitoring with flying robots and artificial intelligence. IEEE Commun. Stand. Mag. 5(4), 48–54 (2021). https://doi.org/10.1109/MCOMSTD.0001.2000080
Chehri, A., Zarai, A., Zimmermann, A., Saadane, R.: 2D autonomous robot localization using fast SLAM 2.0 and YOLO in long corridors. In: Zimmermann, A., Howlett, R.J., Jain, L.C., Schmidt, R. (eds.) Human Centred Intelligent Systems. KES-HCIS 2021. Smart Innovation, Systems and Technologies, vol. 244. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-3264-8_19
Chehri, A., Fortier, P.: Autonomous vehicles in underground mines, where we are, where we are going? In: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), pp. 1–5 (2020). https://doi.org/10.1109/VTC2020-Spring48590.2020.9128585
El Ouahmani, T., Chehri, A., Hakem, N.: Bio-inspired routing protocol in VANET networks—a case study. In: Elsevier’s Procedia Computer Science 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (2019)
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Septfons, B., Chehri, A., Chaibi, H., Saadane, R., Tigani, S. (2022). Swarm Robotics: Moving from Concept to Application. In: Zimmermann, A., Howlett, R.J., Jain, L.C. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 310. Springer, Singapore. https://doi.org/10.1007/978-981-19-3455-1_14
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